Driving style analysis using primitive driving patterns with Bayesian nonparametric approaches

Wenshuo Wang, Junqiang Xi*, Ding Zhao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

111 Citations (Scopus)

Abstract

Driving style analysis plays a pivotal role in intelligent vehicle design. This paper presents a novel framework for driving style analysis based on primitive driving patterns. To this end, a Bayesian nonparametric approach based on a hidden semi-Markov model (HSMM) is introduced to extract the primitive driving patterns from muti-dimensional time-series driving data without prior knowledge of these driving patterns. For the Bayesian nonparametric approach, a hierarchical Dirichlet process (HDP) is applied to learn the unknown smooth dynamical modes in the HSMM, called primitive driving patterns. Two other types of Bayesian nonparametric approaches (HDP-HMM and sticky HDP-HMM) are developed as comparatives in order to show the advantages of the HDP-HSMM. The naturalistic car-following data of 18 drivers are collected from the University of Michigan Safety Pilot Model Deployment database. For each driver, 75 primitive driving patterns are semantically predefined according to their physical and psychological perception thresholds. The individual driving styles are then semantically analyzed based on the distribution over primitive driving patterns, and the similarity of driving styles among drivers is then evaluated. Experimental results demonstrate that the utilization of driving primitive pattern provides a semantically interpretable way to analyze driver's behavior and driving style.

Original languageEnglish
Article number8506402
Pages (from-to)2986-2998
Number of pages13
JournalIEEE Transactions on Intelligent Transportation Systems
Volume20
Issue number8
DOIs
Publication statusPublished - Aug 2019

Keywords

  • Bayesian nonparametric approach
  • Driving style
  • behavioral semantics
  • car-following behavior
  • hidden Markov model

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